A/B testing has long been the bread and butter of conversion rate optimization (CRO). By comparing two versions of a webpage, marketers and designers can identify which changes lead to more conversions, engagement, or other key metrics.
But as powerful as A/B testing can be, it has its limitations. What if you have multiple ideas you want to try out? What if you‘re not sure which element to focus on? That‘s where N testing comes in.
In this ultimate guide, we‘ll dive into everything you need to know about N testing – what it is, how it works, when to use it, and how to analyze your results. Plus, we‘ll highlight some of the top tools and share expert tips to help you run better N tests. Let‘s get started!
What Is N Testing?
N testing, also known as A/B/n testing or multivariate testing, is a type of experiment that compares more than two versions of a webpage or app screen. The "N" stands for the number of variations being tested.
For example, a basic A/B test would compare Version A (the control) with Version B (the challenger). An A/B/C test, on the other hand, would pit three different variations against each other. You can theoretically test as many variations as you want, although most experts recommend limiting it to 3-5 to ensure conclusive results.
Here‘s a simple way to think about it:
- A/B testing = Ordering chocolate or vanilla ice cream
- N testing = Visiting an ice cream shop with 21 flavors and trying several options before picking your favorite
While A/B testing is like flipping a coin, N testing is more like rolling a dice. The more variations you include, the more outcomes are possible.
N Testing vs. Multivariate Testing
You may have heard the terms "N testing" and "multivariate testing" used interchangeably, but there is a key difference.
Multivariate testing involves changing multiple elements on a page at the same time to see which combination performs best. For instance, you might create variations of a product page with different headlines, images, and CTA buttons.
N testing, in contrast, focuses on testing different versions of a single element. The rest of the page stays the same. You might test four headlines while keeping the image and CTA constant.
While multivariate tests can generate powerful insights, they also require much more traffic to reach statistical significance. N testing is a good middle ground between basic A/B tests and complex multivariate ones.
Why Use N Testing?
Now that we‘ve covered the basics of what N testing is, let‘s explore some of the key benefits and reasons to use this approach.
1. Test Multiple Ideas at Once
The biggest advantage of N testing is the ability to evaluate several variations in a single test. This is especially useful when you have multiple viable hypotheses and aren‘t sure which will perform best.
For example, let‘s say you‘re trying to optimize your email capture form. You have a few ideas in mind:
- Variation A: Red CTA button with "Get My Discount" copy
- Variation B: Green CTA button with "Claim My Coupon" copy
- Variation C: Animated CTA button with "Yes, I Want 20% Off!" copy
Instead of running three separate A/B tests, you can run a single A/B/C test and determine the winning CTA much faster. Groupon reportedly increased email signups by 28% using this multivariate approach.
2. Gain Deeper Insights Into Your Audience
Another key benefit of N testing is the ability to learn more about your visitors‘ preferences and behaviors. By presenting them with a wider range of options, you can gain valuable insights into what resonates best.
This is especially useful if you‘re marketing to a new audience segment or testing out a new offer or funnel. N testing can help you quickly identify the most impactful messaging, design, and UX elements.
For instance, HubSpot ran an A/B/C/D test on the homepage hero section of their website. They tested four drastically different designs – one with a photo, one with an illustration, one with a video, and one with a plain colored background.
The variation with the video background emerged as the clear winner, driving a 27% lift in conversions. By testing such distinct creative approaches, the team was able to validate their hypothesis that multimedia content would outperform static images.
3. Find the Biggest Wins
Compared to A/B testing, N testing tends to produce larger uplifts in conversion rates and other key metrics. While an A/B test might generate a 5% improvement, it‘s not uncommon for N tests to result in lifts of 25% or more.
This makes sense when you consider the wider range of options being presented. The more variations you test, the higher the likelihood of finding that one knockout combo that blows the doors off your original version.
Of course, this comes with a caveat – you need to have sufficient traffic to reach statistically significant results. If you only get a few hundred visitors per week, running an A/B/C/D/E/F test is going to take a very long time to conclude.
As a general rule of thumb, most CROs recommend capping the number of variations at 4-5 for most N tests. This provides a good balance between thoroughness and feasibility.
When to Use N Testing
Now that we‘ve sold you on the benefits of N testing, you might be eager to start using it for every experiment. But before you go wild, it‘s important to recognize that N testing isn‘t always the best approach. Here are a few scenarios where it makes sense:
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You have multiple promising ideas. If you have several distinct variations that all seem viable, N testing allows you to evaluate them head-to-head. This is more efficient than running a series of A/B tests.
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You‘re not sure what to prioritize. N testing can help you compare the relative impact of changing different page elements. For instance, does tweaking the headline move the needle more than the hero image?
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You have a high-traffic site. The more variations you test, the more traffic you‘ll need to reach statistical significance. If you get tens of thousands of visitors per day, N testing will be much more feasible than if you only get a few hundred.
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You‘re looking for big wins. If you need to make a splash with your optimization efforts, N testing increases the odds of finding a breakout performer. Just keep in mind that you may need to sift through more losing variations as well.
On the flip side, there are some cases where basic A/B testing is preferable:
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You‘re testing small tweaks. If you‘re just changing a single word in your CTA button copy, a multivariate test is overkill. Stick to A/B testing for minor optimizations.
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You have limited traffic. Low-traffic sites will struggle to generate enough data to conclusively evaluate multiple variations. If you only get a few conversions per day, start with A/B tests before graduating to N tests.
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You‘re not sure what to test. If you don‘t have clear hypotheses in mind, throwing together a bunch of random variations won‘t produce meaningful results. Only run N tests when you have distinct, data-driven ideas.
How to Run an N Test
Ready to start N testing? Here‘s a basic step-by-step process to follow:
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Set Your Goals: What are you trying to optimize? Purchases, signups, clicks, bounce rate, or something else? Make sure you‘re optimizing for a metric that ties to your broader business objectives.
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Form Your Hypotheses: Based on your research, analytics, and customer feedback, develop a set of hypotheses for what you think will improve your key metric. Each hypothesis should propose changing a single element.
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Create Your Variations: Design your variations based on your hypotheses. Use a tool like Adobe XD or Figma to mockup the different versions. Make sure the changes are distinct enough to potentially impact behavior.
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Set Up Your Test: Using your N testing tool of choice (more on those later), create your test and input your variations. Set your traffic allocation and any audience targeting rules.
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QA Your Test: Preview your variations and click through them to ensure everything is working properly. Make sure your tracking code is firing and your goals are being recorded.
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Launch and Monitor: Let your test run until it reaches statistical significance (typically 95% confidence level). Keep an eye on your results to spot any glaring issues or anomalies.
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Analyze the Results: Once your test concludes, dig into the data to determine the winning variation. Look at your conversion rates, revenue per visitor, and other key metrics. Consider segmenting your reports by device, traffic source, or customer cohort.
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Implement and Iterate: Push the winning variation live to all your traffic. But don‘t stop there – use your learnings to inform future tests and continuously optimize your funnel.
Here‘s a hypothetical example of how an ecommerce brand might approach an N test:
Test Setup
- Page: Product Detail Page
- Goal: Increase "Add to Cart" rate
- Primary KPI: Add to Cart button clicks / PDP visits
- Secondary KPIs: Product revenue, bounce rate
- Hypothesis: Highlighting the product‘s key benefit in the headline will increase add to cart rate
- Variations:
- A) Original headline
- B) Benefit-focused headline 1
- C) Benefit-focused headline 2
- D) Benefit-focused headline 3
- Traffic Allocation: 25% to each variation
- Minimum Sample Size: 1,000 visitors per variation
- Target Audience: All desktop traffic
Results
After letting the test run for two weeks, Variation C emerged as the winner with a 95% confidence level:
| Variation | Add to Cart Rate | Relative Improvement |
|---|---|---|
| A (Control) | 6.2% | – |
| B | 6.6% | +6.5% |
| C | 8.1% | +30.6% |
| D | 7.2% | +16.1% |
Not only did Variation C perform 30.6% better than the control, it also beat out the other benefit-focused headlines. This provides the team with valuable insights into what specific messaging resonates best with their audience.
Top N Testing Tools
To run an effective N test, you need the right tools. While you could theoretically code your own solution, it‘s much easier to use a dedicated testing platform. Here are some of the top options on the market:
1. Google Optimize
Google‘s native testing tool is a popular choice for both beginners and advanced users. It‘s free to get started and integrates seamlessly with Google Analytics.
Key features:
- WYSIWYG visual editor
- Advanced targeting and personalization
- Native Google Analytics integration
- Server-side and client-side testing
Pricing: Free version available. Optimize 360 starts at $150,000/year
Best for: Companies of all sizes that use the Google suite of tools
2. Optimizely
Optimizely is a powerful enterprise-grade testing and personalization platform used by top brands like Microsoft, Visa, and Gap.
Key features:
- Advanced stats engine and Multi-Armed Bandit algorithm
- Visual editor with Code Editor option
- Robust audience targeting and segmentation
- Feature management and progressive rollouts
Pricing: Plans start at $50,000/year
Best for: Enterprise organizations needing advanced functionality and support
3. VWO Testing
VWO is an all-in-one CRO platform that offers A/B, multivariate, and split URL testing. Its intuitive interface makes it accessible to marketers and non-technical users.
Key features:
- Drag-and-drop WYSIWYG editor
- Heatmaps, clickmaps, and visitor recordings
- Hyper-targeted campaigns
- Cross-domain testing
Pricing: Plans start at $199/month
Best for: Small to mid-sized businesses looking for a fully-featured CRO tool
4. Convert Experiences
Convert is a cost-effective and user-friendly A/B testing platform used by brands like Sony, Unicef, and Jabra. It boasts a wide range of integrations and solid support.
Key features:
- Point-and-click editor
- Smart segmentation and private audience targeting
- Advanced security and privacy controls
- Extensive integration library
Pricing: Plans start at $699/month
Best for: Security and privacy focused businesses that want enterprise features without enterprise costs
Tips for Better N Tests
To wrap things up, here are some tips and best practices to keep in mind as you embark on your N testing journey:
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Prioritize high-impact pages: Focus your testing efforts on pages that get a lot of traffic and directly impact your bottom line, like your homepage, pricing page, or checkout funnel.
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Test meaningful changes: Avoid testing minor tweaks like button colors or font sizes. Instead, focus on testing headline copy, imagery, social proof, and UX design.
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Use a consistent hypothesis format: For each test, use a format like "If [Variable], then [Result], because [Rationale]" to clarify what you‘re testing and why.
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Minimize confounding factors: Try to isolate the impact of the element you‘re testing by keeping other page elements consistent between variations.
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QA your tests thoroughly: Preview your variations on different devices and browsers to ensure a seamless user experience. Use a tool like Funnelscripts Hero to check your copy for clarity and grammar issues.
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Let tests reach conclusive results: Resist the urge to peek at the results too soon or cut a test short. Wait until you reach at least 95% statistical significance before calling a winner.
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Segment your results: Don‘t just focus on the overall winner. Segment your reports by device category, traffic source, new vs. returning visitors, or customer persona to uncover deeper insights.
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Share your learnings: Document your experiment results and share them with other teams. A culture of testing and learning should extend beyond just the marketing department.
N testing is a powerful way to optimize your marketing campaigns and customer experiences. By comparing multiple variations head-to-head, you can uncover impactful insights and drive meaningful improvements in your KPIs.
While it requires more planning and traffic than basic A/B testing, the potential payoff is well worth it – both in terms of conversion lifts and audience insights.
Remember, the key to successful testing is to start with clear, data-driven hypotheses, focus on high-value pages and meaningful changes, and let your tests run to conclusion.
By embracing N testing as part of your broader CRO programs, you‘ll be well on your way to delivering best-in-class customer experiences that drive meaningful business results.
